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Why every digital-first company needs product analytics

As customers increasingly engage through websites and mobile applications, you need to think harder about the digital customer journey.
As customers increasingly engage through websites and mobile applications, you need to think harder about the digital customer journey. (Pixabay)

The world has seen an explosion of the Digital Economy, and this has only been accelerated by the pandemic. Global online spending has grown from $2.9T in 2020 to $4.2T in 2021. This means that companies now need to look at the digital customer journey in more detail than ever.

The world is shifting towards a digital economy and the pandemic has only hastened this process. A recent report from Adobe estimates that global online spending will hit USD$4.2 trillion in 2021, up from just USD$2.9 trillion in 2020. This underscores the importance of a digital-first approach as you think about the future of your business.

As customers increasingly engage through websites and mobile applications, you need to think harder about the digital customer journey. To improve customer acquisition, conversion, and retention, you need to answer questions like:

- Where are users dropping off in my sign up flow, and why?

- What series of actions are leading to successful paths to conversion?

- How often are my users using my product (active usage)?

- Which user behaviours are driving better retention?

- Are my new website/app features driving conversions, engagement, and retention?

While you probably already have some tools for analytics, they often don’t meet everyone’s needs — particularly product teams that are building the apps and websites that power your digital presence. In fact, our survey of over 450 product managers globally found that over 50% are unable to get answers to product questions quickly and that a further 38% are unable to effectively measure product metrics.

One reason for this is that many organisations are trying to answer product questions only with business intelligence (BI) tools.

Business Intelligence (BI) and product analytics — why you need both

With BI tools, you can query and visualise data from your data warehouses, be it finance, marketing, human resources, or product data. It is perfect for condensing the vast amount of data in an organisation to a few key KPIs but there is a significant drawback.

BI is very flexible in the kinds of data that can be visualised, but as a result, it cannot go as deep into user insights as purpose-built product analytics tools because it is limited to the structure of the data in your data warehouse.

Product analytics allows you to go deeper. By using a standardised data model that captures key aspects of user behaviour (events, users, and other dimensions), product analytics tools like Mixpanel provide a powerful, finely-tuned query engine that answers questions more efficiently than other analytics tools.

As I’ve written before, this benefit of product analytics allows product teams to self-serve analytics, and get answers to complex questions about their users in just seconds. This time of insight is critical in enabling companies to engage in the rapid, exploratory, creative process of product development.

For example, if you wanted funnel analysis to see how users are progressing through your sign-up flow (or not). With SQL, this funnel could take hundreds of lines of SQL to create, but in Mixpanel, it might take just ten clicks.

If you wanted to further examine the users who drop off at a specific stage of the sign-up flow, you will be able to easily save the users who drop off as a cohort and analyse if the cohort changes over time, segment them by user properties, or compare them with other user cohorts to understand how they differ.

With BI tools, the answers to those questions would require a new stream of work from the data engineering team to transform the data into the right format to answer those specific questions. This can sometimes mean a wait of days or even weeks. For many organisations, dedicated data analysts or even data scientists are then tasked to help with this process, which then creates a queue for their time.

Self-serve product analytics helps product teams with time to insight but also prevents data teams, analysts, and data scientists from being saddled with manual work that could distract them from more important and strategic projects.

In this way, product analytics and BI tools can work hand in hand. Many of our customers leverage BI tools to track broader company metrics while using Mixpanel to help their product teams to dive deep into user behaviour, quickly.

The modern data stack and how product analytics fits in

A large up-front investment in data is needed to get value from a BI tool. This often requires a dedicated data team that manages the collection, quality, and transformation of the data into tables that can be efficiently queried by the BI tool, and then also the loading of this data into a data warehouse. This can make it difficult for early-stage companies to implement BI but product analytics can be implemented without these steps.

However, we’ve found that most companies eventually realise that there is a need for them to build their own data stack to support all their data and analytics needs. This means that product analytics should also easily integrate into your modern data stack whenever you choose to build that out.

This can be achieved in a number of ways.

1. Using Customer Data Platforms (CDP) or Software Development Kits (SDK)

One option is to use a CDP that serves as the repository of your user data and feed that data into various applications, including product analytics like Mixpanel. This ensures that from day one, your product teams have access to super powerful product analysis for rapid iteration and growth.

2. Importing data from a data warehouse

We’ve also worked with many companies who create a single source of truth for their company data by putting data from a variety of sources into a single data warehouse. Because the same data is being queried by BI tools and product analytics, this means that the information is identical across tools.

This helps you to create trust in the data as everyone in the organisation (regardless of which tool they’re using), will see the same numbers.

An added advantage of this approach is that you can enrich user behaviour data collected as part of product analytics with a host of other data (e.g. operational data from your CRM systems) to get even more insights into your users.

In the end, product and user insights will be critical for a digital-first strategy and as they say, time is money. The use of a product analytics tool is a key enabler that every digital-first company needs to have.

It is also important to recognise that BI and product analytics aren’t necessarily serving the same purpose — you’re going to need product analytics regardless, and it works great in combination with a BI tool.

This article has been written by Adam Kinney, Principal Product Manager, Mixpanel

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